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Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons

Authors:

Andrew McCallum,

Wei LiAuthors Info & Claims

CONLL '03: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4

Pages 188 - 191

https://doi.org/10.3115/1119176.1119206

Published: 31 May 2003 Publication History

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Abstract

Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).

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Wang YWen YWang YShe XSun XLyu XHao Q(2024)Named entity recognition method for mine electromechanical equipment fieldProceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering10.1145/3672758.3672868(660-664)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3672758.3672868
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https://dl.acm.org/doi/10.1134/S0361768824700233
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CONLL '03: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4

May 2003

213 pages

Conference Chairs:
Walter Daelemans
University of Antwerp and Tilburg University
,
Miles Osborne
University of Edinburgh

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Association for Computational Linguistics

United States

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Published: 31 May 2003

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Conditional Random Fields for Spanish Named Entity Recognition Using Unsupervised Features

Named Entity Recognition in Bengali and Hindi Using MuRIL and Conditional Random Fields

Named Entity Recognition in Hindi Using Conditional Random Fields

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